The second graph confirms that higher MRR scores are obtained for Q m than for Q e .

the query matches the class label at rank 1 and 2 for 50.0% and 17.5% (or a combined 67.5%) of the queries from Q m , with full string matching; and for 52.6% and 12.4% (or a combined 67%), with partial string matching.


They have investigated the properties of these text-based networks with the objective of understanding their structure and applying this knowledge to determine node importance/centrality, connectivity, growth and decay of interest, etc.

the ability to analyze networks, identify influential nodes, and discover hidden structures has led to important scientific and technological breakthroughs such as the discovery of communities of like-minded individuals (New-man and Girvan, 2004), the identification of influential people (Kempe et al., 2003), the ranking of scientists by their citation indexes (Radicchi et al., 2009), and the discovery of important scientific papers (Walker et al., 2006;Chen et al., 2007;Sayyadi and Getoor, 2009).


However, due to increasing computational power and corpus sizes, language modeling today presents a different set of challenges than it did 20 years ago.

modeling crossdomain effects has become increasingly more important (Klakow, 2000;Moore and Lewis, 2010), and deployed systems must frequently process data that is out-of-domain from the standpoint of the language model.


Even though the pattern/instance selection steps in Espresso reduce such a bias to some extent, the bias still persists, as empirically verified by .

the expansion process does not drift in random directions, but tend towards the set of instances and patterns with the highest HITS scores, regardless of the target semantic class.


To apply Espresso for this task, we reformulate the task to be that of seed set expansion, and not classification.

the hand-labeled sentences having the same sense label are used as the seed set, and it is expanded over all the remaining (unlabeled) sentences.


This allows us to completely automate our experiments, without the need to manually check the HITS ranking in Step 2 of Section 3.2.

for the most frequent sense (majority sense), we take Step 3a and use the highest ranked instances as seeds; for the rest of the senses (minority senses), we take Step 3b and use them as the stop list.


As shown in the table, our method (HITS) outperforms the baseline not using a stop list (NoStop), in all evaluation metrics.

the P@n listed in Table 2 shows that our method provides about 11 percentage points absolute improvement over the baseline on interest, for all n = 10, 20, and 30.


The generalization of the nearest neighbor algorithm, k nearest neighbor, finds the k most similar data points T k to x and assigns x the labelŷ such that:ŷ = arg max Euclidean distance and || • || = 1 if the argument is true (else 0).

the k most similar points take a weighted vote on the class of x.


Several strategies have been proposed to make nearest neighbor classification more efficient (Angiulli, 2005).

training set condensation techniques have been much studied.


The implementation used in the experiments builds on Orange 2.0b for Mac OS X (Python and C++).

we made use of the implementations of Euclidean distance and random sampling in their package.


A grammar G is a particular finite subset of A and the language of the grammar is all words which f maps to a subset of G. For L T,k , the grammar can be thought of as the set of permissible factors on tier T and the function is w → F k (⋊E T (w)⋉).

every word is mapped to the set of k-factors present on tier T .


In this paper, we explore more linguistically motivated approaches to vandalism detection.

we hypothesize that textual vandalism constitutes a unique genre where a group of people share a similar linguistic behavior.


Therefore, we can apply methods from metaphor identification to TWSS identification.

we (1) compare the adjectival selectional preferences of sexually explicit nouns to those of other nouns to determine which nouns may be euphemisms for sexually explicit nouns and 2examine the relationship between structures in the erotic domain and nonerotic contexts.


While this was the only existing classifier we were able to find, this was not a rigorously approached solution to the problem.

its training data were noisy, partially untaggable, and multilingual.


The first baseline simply uses the single dialogue act label chosen by the maximum entropy classifier as the only dialogue act for each utterance.

this baseline corresponds to the NLU developed for the SASO dialogue system (Traum et al., 2008b) by Sagae et al.


We investigate systems that identify opinion expressions and assigns polarities to the extracted expressions.

we demonstrate the benefit of integrating opinion extraction and polarity classification into a joint model using features reflecting the global polarity structure.


This phenomenon, known in the literature as entrainment, alignment, accommodation, or adaptation has been found to occur along many acoustic, prosodic, syntactic and lexical dimensions in both human-human interactions (Brennan and Clark, 1996;Coulston et al., 2002;Reitter et al., 2006;Ward and Litman, 2007;Niederhoffer and Pennebaker, 2002;Ward and Mamidipally, 2008;Buder et al., 2010) and humancomputer interactions (Brennan, 1996;Bell et al., 2000;Stoyanchev and Stent, 2009;Bell et al., 2003) and has been associated with dialogue success and naturalness (Pickering and Garrod, 2004;Goleman, 2006;Nenkova et al., 2008).

interlocutors who entrain achieve better communication.


We term consecutive pairs of IPUs from a single speaker holds, and contrast hold-preceding IPUs with backchannel-preceding IPUs to isolate cues that are significant in preceding backchannels.

when a speaker pauses without giving up the turn, which IPUs are followed by backchannels and which are not?


The main idea is to use unlabeled data in both domains to learn linear predictors for many "auxiliary" tasks, which should be somewhat related to the task of interest.

if x is a row vector representing the original feature vector and y i represents the label for auxiliary task i, the linear predictor w i is learned to predictŷ i = w i • x ′ (where x ′ is a modified version of x that excludes any features completely predictive of y i .)


However, such anecdotal evidence cannot fully support claims about general behavior in query formulation.

this upward trend could be due to changes in the kind of information users are now seeking from the Web, e.g., as a result of growing popularity of Q&A sites or as people entrust search engines with more complex information needs; supporting the latter, in a very recent study, Aula et al.


C all consists of queries formulated for similar search intent, where intent was approximated by the result URL clicked in response to the query.

we assume queries that lead to a click on the same URL are issued with similar information need.


We remove the word used for Q-query identification from the input string to avoid trivial observations.

"how find network key" becomes "find network key", with zero contribution to the probability mass of function words.


), it is statistically significant that Q-level is growing over time: the null hypothesis is rejected with P < 0.001.

over a large collection of intents and contents, users are becoming more likely to formulate queries in question forms, even though such content could easily be reached via non-question-queries.


It is important to notice that while most theoretical papers talk about the collectivity vs. distributivity distinction at the sentence level, for us the right treatment is to make this distinction at the constraint level.

a plural may have a collective reading in one constraint but a distributive reading in another, as shown in example 2 in figure (3).


Then Kernelized Sort-ing formulates Π as the solution of the following optimization problem (Gretton et al., 2005): In our supervised version of Kernelized Sorting, we fix the permutation matrix (to sayΠ) and modify the kernel matrices K x and K y so that the objective function is maximized for the given permutation.

we find a mapping for each language, such that when the documents are projected into their common subspaces they are more likely to respect the alignment given byΠ.


Our experimental results reveal that character-level me-trics correlate with human assessment better than word-level metrics.

cWS is not essential for automatic evaluation of chinese translation output.


A pair (V, I) is called a matroid if V is a finite ground set and I is a nonempty collection of subsets of V that are independent.

i must satisfy (i) if X ⊂ Y and Y ∈ i then X ∈ i, (ii) if X, Y ∈ i and |X| < |Y | then X ∪{e} ∈ i for some e ∈ Y \X.


We typically refer to a matroid by listing its ground set and its family of independent sets: M = (V, I).

a set function f : 2 V → R is called submodular (Edmonds, 1970) if it satisfies the property of diminishing returns: the incremental "value" of v decreases as the context in which v is considered grows from X to Y .


Our matroid generalizations provide flexibility in modeling fertility, and also strategies for solving the word alignment problem efficiently and near-optimally.

when f is monotone submodular, near-optimal solutions for Problem 1 can be efficiently guaranteed.


For example, in (Fisher et al., 1978), a simple greedy algorithm for monotone submodular function maximization with a matroid constraint is shown to have a constant approximation factor.

the greedy algorithm finds a solution A such that f (A) ≥ 1 m+1 f (A * ) where A * is the optimal solution and m is number of matroid constraints.


5) was used in our experiments in order for no feature weight learning to be required.

we estimated the score of aligning i to j as where the translation probability p(f j |e i ) and alignment probability p(i|j, I) were obtained from IBM model 2 trained on the 1.1M sentences.


As mentioned in Section 3, matroid constraints generalize the matching constraint.

when using two matroid constraints, Fert E (A) ≤ 1 and Fert F (A) ≤ 1, we have the matching constraint where fertility for both English and French words are restricted to be at most one.


By using the word-dependent matroid constraint (setup 3 and 5), AERs are reduced compared to those using global matroid constraints.

18.6% AER is achieved by setup 5, which significantly outperforms the maximum weighted bipartite matching approach.


We include similar information in our model.

we calculate the number of left and right modifiers separately, calling them left valency and right valency, respectively.


This is because the desirable credit-assignment properties of our joint training procedure are achieved through duplication (Zadrozny et al., 2003).

the LSVM knows that a specific event is important because it appears in event sets Z a for many arcs from the same sentence.


2, all the derivation information is embedded in each symbol.

nP (nP (DT the) (n girl)) is a root symbol of the initial tree "(nP (DT the) (n girl))", which generates two child nodes: DT (DT the) and n (n girl) .


Automatic text summarization aims at reducing the amount of text the user has to read while preserving important contents, and has many applications in this age of digital information overload (Mani, 2001).

query-oriented multi-document summarization is useful for helping the user satisfy his information need efficiently by gathering important pieces of information from multiple documents.


We use all the candidate documents from which nuggets were extracted as input to the multidocument summarizers.

in our problem setting, the relevant documents are already given, although the given document sets also occasionally contain documents that were eventually never used for nugget extraction (Mitamura et al., 2008;Mitamura et al., 2010).


The model is defined in terms of state transition probabilities P(c | c), i.e., the probability of transitioning from a state labeled c to a state labeled c ; and state observation probabilities P(w | c), i.e., the probability of emitting word w from a particular class c. Since the classes are predicting an ordering, we include hard constraints on class transitions.

we forbid a transition from a class closer to the head noun to one farther away.


More recently, first steps have been taken towards treating dialogue generation as an instance of Textto-Text generation (Rus et al., 2007).

the T2D system (Piwek et al., 2007) employs rules that map text annotated with discourse structures, along the lines of Rhetorical Structure Theory (Mann and Thompson, 1988), to specific dialogue sequences.


For example, Expl(ain) can be generated by simply copying a monologue segment to dialogue utterance.

the dialogue acts Yes and Agreement can be generated using canned text, such as "true" and "I agree with you".


13 Ratio of approximated numbers We count the instance of approximated numbers in text.

we count the pattern more than/less than/about/almost ¡integer number¿.


To extract a candidate tuple with an unknown term, the term must also be a valid candidate of its associated category.

the term must match at least one pattern assigned to the category and not match patterns assigned to another category.


Over the top 1000 terms, RGB significantly outperforms the corresponding WMEB with and without negative categories (p < 0.05).

3 inter-RGB significantly improves upon WMEB with no negative categories (501-1000: +13.5%, 1-1000: +8.8%).


In this paper we explore whether using coreference can improve the learning process.

if the algorithm considered texts like his birth in 1756 for the above relation, would performance of the learned patterns be better?


Given an n-gram language model, we only calculate the scores in windows with the size 2n − 2, centered on the boundary of the substituted phrases.

n − 1 words before and after the boundaries will be included in the calculation.


On the other hand, our choice was driven by the availability of large corpora for English.

we used the New York Times Annotated Corpus.


In order to be able to explore our approach in a fruitful manner, we decided to concentrate on words which have acquired a new dimension of use due to the introduction of computing and the internet, e.g., to browse, to surf, bookmark.

the Netscape Navigator was introduced in 1994 and our data show that this does indeed correlate with a change in use of these words.


In order to be able to judge the effectiveness of our new approach, we chose key words that are likely candidates for a change in use in the time from 1987 to 2007.

we concentrated on terms relating to the relatively recent introduction of the internet.


Supporting such fluid interactions has become an important topic of virtual human research.

backchannel feedback has received considerable interest due to its pervasiveness across languages and conversational contexts.


Instead, the evaluation scheme, similar to the pyramid scheme used for summarization evaluation, assigns scores to each example based on its level of agreement among the annotators.

each example is assigned positive and negative scores, p = n + /N and n = n -/N, where n + is the number of annotators that annotate the example as positive, and nfor the negative.


Our precision and recall goes with the same spirit with traditional precision and recall, as a result, performance decreases with the decrease of information.

i. if we remove relations from the reference annotation and then compare that against the full reference annotation, then recall decreases linearly.


The sentences it got wrong all failed because of missing medical terms.

atrium and vena cava caused lexical errors.


The italicized number beneath each score is the improvement over the BASELINE approach from Table 3. the Parliament proceedings nor our list of high frequency OOV terms.

if our dictionary mining system found as-good translations for the words in its list as the (cheating) oracle system, this is how well it would do.


For our decoding time complexity computation, only the parameter c value can be affected by our similarity-based scheme.

our similaritybased scheme would result in a larger c value at decoding time as many similar translation rules might be matched at each node.


Besides, to speed up decoding long sentences, it seems a feasible solution to first divide a long sentence into multiple short sub-sentences for decoding, e.g., based on comma.

we can segment a complex source-language parse tree into multiple smaller subtrees for decoding, and combine the translations of these small subtrees to form the final translation.


In the last factor, some POS tags are further refined (Table  1).

we will use all possible trigrams where each element is either a surface from, a POS, or an extended POS.


Assuming we are able to compute a M × Ndimensional bilingual word co-occurrence matrix Σ from the SMT parallel corpus, the matrix-vector productû = Σv is a projection of the target sentence in the source vector space.

those source terms of the M -dimensional vectorû will be emphasized that most frequently co-occur with the target terms in v. ,û can be interpreted as a "bagof-words" translation of v. the cross-lingual similarity between the candidate source and target sentences then reduces to the cosine similarity between the source term-frequency vector u and the projected target term-frequency vectorû, as shown in Equation 2.1: In the above equation, we ensure that both u and u are normalized to unit L 2 -norm.


In this pilot modeling experiment, we explore a (limited) set of cues which can be used to predict the V vs. T dichotomy for English sentences.


Nevertheless, some features point towards more general patterns.

we observe titles among the V-indicators (gentlemen, madam, ma+'am) as well as formulaic language (Permit (me)).


In the task of bilingual lexicon extraction, two document sets are similar to each other and should be clustered if the combination of the two can complement the content of each single set, which relates to the notion of homogeneity.

both the English part C e 1 of C 1 and the French part C f 1 of C 1 should be comparable to their counterparts (respectively the same for the French part C f 2 of C 2 and the English part C e 2 of C 2 ).


Two words are potential translation candidates if they are often present in the same cross-lingual topics and not observed in other cross-lingual topics.

a word w 2 from a target language is a potential translation candidate for a word w 1 from a source language, if the distribution of w 2 over the target language topics is similar to the distribution of w 1 over the source language topics.


In this paper, as a first step in the project, we focus on content-related criteria.

we chose to start with the aspect of "coverage".


In fact, a high quality report shares many of the characteristics of a good document summary.

it seeks to cover as much of the important information as possible, while avoiding redundancy and irrelevant information.


We are in the process of obtaining more documents in the domain, which will allow the use of more complex models and more sophisticated representations.

we are considering clusters of terms and probabilistic topic models such as LDA (Blei et al., 2003).


Our definition of the complexity of a word is based on two measures: the corpus complexity and the lexical complexity.

we define the corpus complexity of a word as where f w,c is the frequency of word w in corpus c, and the lexical complexity as L w = |w|, the length of the word.


The vector contains all features common to both words, with the feature values that are the minimum between them.

ccV w, We calculate the cosine similarity of the common context vector and the sentence context vector: If the context similarity is larger than a threshold (0.01), we use this rule to simplify.


In this paper, we examine prior techniques that have been used to successfully rank helpfulness for product reviews, and adapt them to the peer-review domain.

we use an SVM regression algorithm to predict the helpfulness of peer reviews based on generic linguistic features automatically mined from peer reviews and students' papers, plus specialized features based on existing knowledge about peer reviews.


The goal of this paper is to address the above issues by using crowdsourcing, which has been proven effective for collecting multiple, reliable judgments in other NLP tasks: machine translation (Callison-Burch, 2009;Zaidan and Callison-Burch, 2010), speech recognition (Evanini et al., 2010;Novotney and Callison-Burch, 2010), automated paraphrase generation (Madnani, 2010), anaphora resolution (Chamberlain et al., 2009), word sense disambiguation (Akkaya et al., 2010), lexicon construction for less commonly taught languages (Irvine and Klementiev, 2010), fact mining (Wang and Callison-Burch, 2010) and named entity recognition (Finin et al., 2010) among several others.

we make a significant contribution to the field by showing how to leverage crowdsourc-ing to both address the lack of appropriate evaluation metrics and to make system comparison easier.


Because this sub-system models the real-life friend groups, we only consider bi-directional following relation between people.

we only consider an edge between users when both of them follow each other.


We use information about the surrounding characters (character and character-type n-grams), as well as the presence or absence of words in the dictionary as features (Table 2).

dictionary features for word segmentation l s and r s are active if a string of length s included in the dictionary is present directly to the left or right of the present word boundary, and i s is active if the present word boundary is included in a dictionary word of length s. Dictionary feature d jk for POS estimation indicates whether the current word w j occurs as a dictionary entry with tag t k .


We first generate a merged grammar G ′ using collected subtrees under adapted nonterminals, then use synchronous parsing to obtain probabilities in the segmentation lattice.

we "flatten" the collected subtrees under Syl, i.e.


In this point of view, the proposed method is language independent.

we are interested in the English token "x", which is the only one that has two corre-  sponding Chinese characters (" [ke si]").


While the probabilistic and coverage models are comparable on RTE-6 (with non-significant advantage for the former), on RTE-5 the latter performs better, suggesting that the probabilistic model needs to be further improved.

wN 4 performs better than the single-step wN only on RTE-5, suggesting the need to improve the modeling of chaining.


Recently, Täckström and McDonald (2011) showed that fine-grained sentiment can be learned from coarse-grained supervision alone.

they used a HCRF model with the same structure as that in Figure 1a, but with sentence labels treated as latent variables.


Although more complex representations are possible, we generate meta-features for each sentence based solely on operations on the estimated distributions, p θ C (y d , y s i |s).

we encode the following probability distributions as discrete features by uniform bucketing, with bucket width 0.1: the joint distribution, p θ C (y d , y s i |s); the marginal document distribution, p θ C (y d |s); and the marginal sentence distribution, p θ C (y s i |s).


An intuitive framework for spoken dialogue system (SDS) can be regarded as a chain process.

the automatic speech recognition (ASR) module accepts the user's utterance U t and returns a string of words W t The spoken language understanding (SLU) module converts W t to an abstract representation of the user's dialogue act (DA).


The estimated bi-gram model is used to calculate the history score.

essentially, (6) is based on a Markov model assumption for the chain of the dialogue acts.


In addition, words that are not confidently recognized are replaced by a special non-keyword token called Filler.

we compute the z-score (Larsen and Marx, 2000) of each word w in the ASR output.


In this work, the spectral clustering algorithm (von Luxburg, 2007) is employed for sentence clustering.

suppose we have n vectors represented as C = {v k v(s k ), k = 1, .


We will therefore refer to our model as the variable mixture model (VMM).

our model differs from linear or log-linear interpolation models (Klakow, 1998), which combine a typically small number of components that are common across instances.


In order to improve generalization, we estimate the categorical parameters based on the counts from all instances, except the one whose gradient is being computed for the online update (leave-one-out).

we subtract the counts for a particular instance before computing the update (Equation 8) and add them back when the update has been executed.


The third feature set (long-range, LR) is an extension of SR which also includes longerdistance features.

this feature set additionally includes all unigram bag features up to a distance d = 9.


The order of a VMM is defined through the length of the context from which the basic and short-range features are extracted.

vM-BA of a certain order uses the same features as the N-Gram models of the same order and vM-SR uses the same conditioning context as the N-Gram models of the same order.


What's interesting is while the heuristic algorithm that is based entirely on syntactic structure produced a strong baseline, when formulated as features they are not at all effective.

feature groups 7, 8, 9 are explicit reformulations of the heuristic algorithm, but they all contributed very little to or even slightly hurt the overall performance.


One simple but powerful approach for further enhancing the performance is to utilize a large amount of unsupervised data to supplement supervised data.

an approach that involves incorporating 'clusteringbased word representations (CWR)' induced from unsupervised data as additional features of supervised learning has demonstrated substantial performance gains over state-of-the-art supervised learning systems in typical NLP tasks, such as named entity recognition (Lin and Wu, 2009;Turian et al., 2010) and dependency parsing (Koo et al., 2008).


As described in Section 2, we define the m-th condensed feature h m (x, y) as the summation of all the original features f n assigned to S m .


For a fair comparison, we prepared L 1 -regularized supervised learning algorithms, which try to reduce the non-zero parameters in a model.

we utilized L 1 -regularized CRF (L1CRF) optimized by OWL-QN (Andrew and Gao, 2007) for NER, and the online structured output learning version of FOBOS (Duchi and Singer, 2009;Tsuruoka et al., 2009) with L 1regularization (ostL1FOBOS) for dependency parsing.


This paper has described using a domainindependent document modeling approach of avoiding low-content syntax words in an NLP task where high-content semantic words should be the principal focus.

we have shown that we can increase summarization performance by modeling the document set probability distribution using a hybrid LDA-HMM content and syntax model.


a simple paragraph and one or more normal paragraphs), we then used a dynamic programming approach to find that best global sentence alignment following Barzilay and Elhadad (2003).

given n normal sentences to align to m simple sentences, we find a(n, m) using the following recurrence: where each line above corresponds to a sentence alignment operation: skip the simple sentence, skip the normal sentence, align one normal to one simple, align one normal to two simple, align two normal to one simple and align two normal to two simple.


By repeatedly resampling from this distribution we obtain individual word assignments which in turn allow us to estimate the topic multinomials and the per-document mixing proportions.

the topic multinomials are estimated as: while the per-document mixing proportions θ d can be estimated as: Although we experimented with hyper-parameter learning (Dirichlet concentration parameter η), doing so did not significantly impact the final model.


Most previous work in cross-language adaptation has used parallel corpora to project dependency structures across translations using word alignments (Smith and Eisner, 2009;Spreyer and Kuhn, 2009;Ganchev et al., 2009), but in this paper we show that similar results can be achieved by much simpler means.

we build on the cross-language adaptation algorithm for closely related languages developed by Zeman and Resnik (2008) and extend it to much less related languages.


Of course annotations differ, but nouns are likely to be dependents of verbs, prepositions are likely to be dependents of nouns, and so on.

zeman and Resnik (2008) trained a constituent-based parser on the training section of the Danish treebank and evaluated it on sentences of up to 40 words in the test section of the Swedish treebank and obtained an F 1 -score of 66.40%.


We are interested not in the derivation tree for an entire sentence, but rather only that slice of it having etrees with words that are in the nucleus being examined, which we call the derivation tree fragment.

for a given nucleus being examined, we partition its instances based on the covering node in the full tree, and within each set of instances we compare the derivation tree fragments for each instance.


This is most notably apparent in the verbal phrases and sentential levels.

while most sentences follow an SVO order, OVS and VSO configurations are also possible.


This feature schema covers three feature functions: , which decide whether partial parses on stack S correspond to a constituent in the source-style parse t s .

f c (s i , t s ) = + if s i has a bracketing match (ignoring grammar labels) with any constituent in t s .


The Viterbi dynamic program for finding the shortest derivation is quite simple: it requires CKY to store only bytevalued switch-counts s(X p , i, j) (i.e., the number of switches) for each chart item and compute the derivation with the least switch-count.

in the dynamic program, if we use a SWITCH rule X p → X q , then we update If we use a continue rule X p → Y q Z r , then the update is where k is a split point in the chart.


Lines 7-8 is the critical part: it gives a "label" to each hypothesis, based on whether it is in the Pareto Frontier.

first we call FindParetoFrontier (Algorithm 1), which returns a set of pareto hypotheses; pareto-optimal hypotheses will get label 1 while non-optimal hypotheses will get label 0.


Following (Croarkin and Tobias, 2006, Section 4.4.5.2), the anchor confidence is set to be the inverse of the cross-validated mean square residuals: where w \c j are the feature weights obtained by the regression above on all training configurations except c, µ jct is the gold value at anchor j for training/test combination c, t, and N is the total number of such combinations 7 .

we assign to each anchor point a confidence inverse to the crossvalidated mean squared error of the model used to predict it.


On the other hand, there are significant speedup in the runtime of HVR systems.

synchronous HVR achieved the best runtime performance, which is roughly consistent across different noise conditions (approximately 1.0 xRT).


2, where the shaded circle denotes the observed feature vectors, and the squares denote the hyperparameters of the priors used in our model.

we use a Bernoulli distribution as the prior of the boundary variables and impose a Dirichlet process prior on the cluster labels and the HMM parameters.


We believe it is because the superiority of the improved model is disguised by the ASR error.

the impact of ASR error under the FST model is more significant than the improvement of the FST model.


For nouns, we extract all pairs of nouns from (S L , S R ), and compute the average similarity among these pairs.

we use path similarity, lch similarity, wup similarity, res similarity, jcn similarity, and lin similarity provided in the nltk.wordnet.similarity package (Bird et al., 2009) for computing WordNet-based similarity, and always choose the most frequent sense for each noun.


Hybrid: Trained and tested on all text span pairs.

we split the training set and the testing set following the convention of RST-DT, and conduct Structure and Relation classification by incorporating our rich linguistic features, as listed in Section 5.3 above.


In contrast, if we apply the same scheme to the Chinese sentence, no discourse relation will be picked out because this is just one comma-separated sentence with no explicit discourse connectives in it.

the discourse relation within the Chinese sentence, which would be captured in its English counterpart following the PDTB procedure, would be lost when annotating Chinese.


It should be noted that annotating implicit relations directly with their senses means that sense annotation is no longer restricted to those that can be lexically expressed, but also includes those that cannot, notably those labeled "EntRel/NoRel" in the PDTB.

2 we annotate senses of discourse relations, not just connectives and their lexical alternatives (in the case of AltLex).


In Section 2, we incorporate participant identity in a new model we call Speaker Identity for Topic Segmentation (SITS), which discovers topical structure in conversation while jointly incorporating a participantlevel social component.

we explicitly model an individual's tendency to introduce a topic.


As the parsing community trains on sections 2-21 of the Penn Treebank (Marcus et al., 1993) and tests on section 23, we create Gigaword sections by isolating specific months.

jan-May and Sep-Dec Development july june and August the development set includes documents from july 1995, july 1996, july 1997.


In our particular implementation: • We use TARSQI to extract temporal expressions and link them to events.

tARSQI uses the following temporal links: included, simultaneous, after, before, begun by or ended.


Table 2 shows our particular mapping between temporal links and constraints.

for the default document creation time, we suppose that a relation which appears in a document with creation time d held true at least in that date; that is, we are assuming a within link, and we map t 2 = d, t 3 = d. Inter-document temporal evidence aggregation.


Our results demonstrate the strength of our relation extraction technique -while using planning feedback as its only source of supervision, it achieves a precondition relation extraction accuracy on par with that of a supervised SVM baseline.

it yields an F-score of 66% compared to the 65% of the baseline.


Parameter Update Parameter updates in our model are done via reinforcement learning.

once the model has predicted a subgoal sequence for a given goal, the sequence is given to the low-level planner for execution.


termining the color of an object by the nearness of the noun denoting the object to the color term.

we are trying to model the meaning of color terms and how they relate to other words, and not to directly extract the color of an object from pictures depicting them.


The CRF tagger model defines a conditional distribution p(τ |e; θ tag ) for a class sequence τ given a sentence e and model parameters θ tag .

the sample space is over class-not word-sequences.


Agendas can further be trimmed based on a histogram beam (Saers et al., 2009) or probability beam  compared to the best hypothesisâ.

we have a queue discipline based on the inside probability, and all spans a k where I(a k ) < cI(â) are pruned.


Figure 1 (a) provides an example of why it is unwise to ignore competing hypotheses during beam pruning.

the alignment "les/1960s" competes with the high-probability alignment "les/the," so intuitively should be a good candidate for pruning.


w n at position i is defined as the set of all paths in the lattice that may be obtained by editing w i : deleting it, substituting it, or inserting a word to its left.

it is the "distance-1-at-position i" neighborhood of w. Given a position i in a word sequence w, all hypotheses in N (w, i) are rescored using the longspan model and the hypothesisŵ (i) with the highest score becomes the new w. The process is repeated with a new position -scanned left to right -until w =ŵ (1) = .


These methods typically constrain the parsing space using heuristics, which are often model specific.

they search in the joint space of word sequences present in the lattice and their syntactic analyses; they are not guaranteed to produce a syntactic analysis for all hypotheses.


In this work, the weighted alternatives are then used in a standard (generative) speech recognizer.

these approaches optimize generative models using discriminative criteria.


Inspired by the sub-word tagging method introduced in (Sun, 2011), we propose a structure-based stacking model to fully utilize heterogeneous word structures to reduce the approximation error.

joint word segmentation and POS tagging is addressed as a two step process.


3561 word tokens are consistently segmented by both standards.

91.7% CTB word tokens share the same word boundaries with 91.6% PPD word tokens.


Following (Sun, 2011), the intermediate sub-word structures is defined to maximize the agreement of CTag ctb and CTag ppd .

the goal is to make merged sub-words as large as possible but not overlap with any predicted word produced by the two taggers.


The stacking models can be viewed as annotation converters: They take as input complementary structures and produce as output target structures.

the stacking models actually learn statistical models to transform the lexical representations.


(2009) present a preliminary study for the annotation adaptation topic, and conduct experiments with the extra PPD data 4 .

the CTB-sytle annotation is the target analysis while the PPD-style annotation is the complementary/auxiliary analysis.


The error is reduced significantly on all data sets.

the word cluster features can significantly reduce the amount of labeled data required by the learning algorithm.


Our proposal is based on the intuition that a feature with higher frequency in the training process should be with a learning rate that decays faster.

we assume a high frequency feature observed in the training process should have a small learning rate, and a low frequency feature should have a relatively larger learning rate in the training.


However, the definition of models for optimally combining lexical and syntactic constraints is still far for being accomplished.

the exhaustive design and experimentation of lexical and syntactic features for learning verb classification appears to be computationally problematic.


the syntactic behavior, and better capture paradigmatic relations, such as synonymy.

word space models, as described in (Sahlgren, 2006), define contexts as the words appearing in a n-sized window, centered around a target word.


In this paper we model verb classifiers by exploiting previous technology for kernel methods.

we design new models for verb classification by adopting algorithms for structural similarity, known as Smoothed Partial Tree Kernels (SPTKs) (Croce et al., 2011).


Translation consensus is employed in those minimum Bayes risk (MBR) approaches where the loss function of a translation is defined with respect to all other translation candidates.

the translation with the minimal Bayes risk is the one to the greatest extent similar to other candidates.


, is the propagating probability in equation 8, with the similarity measure , defined as the Dice co-efficient over the set of all n-grams in and those in .

, , where is the set of n-grams in string , and , is the Dice co-efficient over sets and : We take 1 4 for similarity between translation candidates, thus leading to four features.


Figure 2: Smoothed-ℓ 0 alignments (red circles) correct many errors in the baseline GIZA++ alignments (black squares), as shown in four Chinese-English examples (the red circles are almost perfect for these examples, except for minor mistakes such as liu-shūqīng and meeting-zàizuò in (a) and .-, in (c)).

the baseline system demonstrates typical "garbage-collection" phenomena in proper name "shuqing" in both languages in (a), number "4000" and word "láibīn" (lit.


In all four cases, the baseline produces incorrect extra alignments that prevent good translation rules from being extracted while the smoothed-ℓ 0 results are correct.

the baseline system demonstrates typical "garbage collection" behavior (Moore, 2004) in all four examples.


We now evaluate the proposed TME and ME-TME models.

we evaluate the discovered C-expressions, contentious aspects, and aspects often mentioned in questions.


The "adjusted" (AD) score does not count mistakes due to extraction errors.

if an inference is incorrect because it was based on incorrect extracted facts, we remove it from the set of inferences and calculate precision for the remaining inferences.


A key insight in our approach is to reduce content selection and surface realization into a common parsing problem.

we define a probabilistic context-free grammar (PCFG) that captures the structure of the database and its correspondence to natural language.


A commonality in many of these approaches is that they model the problem of entity coreference as a collection of decisions between mention pairs (Bagga and Baldwin, 1999;Soon et al., 2001;McCallum and Wellner, 2004;Singla and Domingos, 2005;Bengston and Roth, 2008).

coreference is solved by answering a quadratic number of questions of the form "does mention A refer to the same entity as mention B?"


To address semantic ambiguities in coreference resolution, we use Web n-gram features that capture a range of world knowledge in a diffuse but robust way.

we exploit short-distance cues to hypernymy, semantic compatibility, and semantic context, as well as general lexical co-occurrence.


Instead of collecting the contexts of each mention and creating sparse features from them, we use Web-scale distributional clustering to summarize compatibility.

we begin with the phrase-based clusters from , which were created using the Google n-grams V2 corpus.


Given a discussion thread with d discussants and e entity targets, each attitude profile vector has n = (d + e) * 3 dimensions.

each target (discussant or entity) has three corresponding values in the DAP: 1) the number of times the discussant expressed positive attitude toward the target, 2) the number of times the discussant expressed a negative attitude towards the target, and 3) the number of times the the discussant interacted with or mentioned the target.


Building a lexicon of the formal meaning representations of words and phrases, either implicitly or explicitly, is usually an important step in inferring the meanings of entire sentences.

chen and Mooney (2011) first learned a lexicon to help them resolve ambiguous supervision of relational data in which the number of choices is exponential.


From the corresponding navigation plan, we find all connected subgraphs of size less than or equal to m. We then update the cooccurrence counts between all the n-grams w and all the connected subgraphs g. We also update the counts of how many examples we have encountered so far and counts of the n-grams w and subgraphs g. At any given time, we can compute a lexicon using these various counts.

for each n-gram w, we look at all the subgraphs g that cooccurred with it, and compute a score for the pair (w, g).


As with previous work on TSG induction, our task is the induction of SR-TSG derivations from a corpus of parse trees in an unsupervised fashion.

we wish to infer the symbol subcategories of every node and substitution site (i.e., nodes where substitution occurs) from parse trees.


1, (A) is one re-writing rule that can be applied into the sentence re-writing (B).

we propose a new class of kernel functions (Schölkopf and Smola, 2002), called string rewriting kernel (SRK), which defines the similarity between two re-writings (pairs) of strings as the inner product between them in the feature space induced by all the re-writing rules.


Given a new string re-writing (s,t) ∈ Σ * × Σ * , our goal is to predict its response y.

the training data consists of binary classes of string re-writings, and the prediction is made for the new re-writing based on learning from the training data.


HTMM can also be viewed as a soft clustering tool for words in training corpus.

hT-MM can estimate the probability distribution of a topic over words, i.e.


Noticing that there are some words occurring in one corpus only, we use the words belonging to both corpora during the mapping procedure.

we decompose P (t f out |t f in ) as follows: Here we first get P (f |t f in ) directly from the topic model related to C f in .


Now we briefly describe how to estimate the adapted lexical weight for phrase pairs, which can be adjusted in a similar way to the phrase probability.

adopting our method, each word is considered as one phrase consisting of only one word, so Here we obtain w(e|f, t f out ) with a similar approach to φ(ẽ|f , t f out ), and calculate P (t f out |t f in ) and P (t f in |f ) by resorting to formulas (8) and (9).


Applying our adaptation approach, both "AdapBP" and "OutBP + AdapBP" consistently outperform the baseline, and the lat- ter produces further improvements over the former.

the BLEU scores of the "OutBP + AdapBP" method are 20.70 and 21.20, which obtain 0.48 and 0.98 points higher than the baseline method, and 0.16 and 0.09 points higher than the 'AdapBP" method.


In experiments not reported here, we obtained accurate rankings by scoring source permutations with a uniformly weighted combination of two LMs trained on chunk types and on chunk-type+headword, respectively.

3-best reorderings of each rule-matching sequence yield reordering recalls of 77.2% in Arabic and 89.3% in German.


This paper makes the connection concrete with a tree transducer based semantic parsing model and suggests that other models can be interpreted in a similar framework, increasing the generality of their contributions.

this paper further introduces a variational Bayesian inference algorithm that is applicable to a wide class of tree transducers, producing state-of-the-art semantic parsing results while remaining applicable to any domain employing probabilistic tree transducers.


The tree transducer defines a joint distribution over the input y, output w, and their derivation x as the product of the weights of the rules appearing in x.

where θ is the set of multinomial parameters, r is a transducer rule, θ(r) is its weight, and c r (x) is the number of times r appears in x.


Since the integral in the denominator is intractable, we look for an appropriate approximation q(θ, X ) ≈ p(θ, X |Y, W).

we assume the rule weights and the derivations are independent, i.e., q(θ, X ) = q(θ)q(X ).


A GREIBACH normal form for a superclass of CFTG (viz., second-order abstract categorial grammars) was discussed by Kanazawa and Yoshinaka (2005) and Yoshinaka (2006).

they also demonstrate that monadic CFTG can strongly lexicalize regular tree grammars (Gécseg and Steinby, 1984;Gécseg and Steinby, 1997).


The CFTG G is start-separated if pos S (r) = ∅ for every production → r ∈ P .

the start nonterminal S is not allowed in the right-hand sides of the productions.


It requires that the right-hand side of each non-initial production contains at least two terminal or nonterminal symbols.

it eliminates projection productions A(x 1 ) → x 1 and unit productions, in which the right-hand side has the same shape as the lefthand side (potentially with a different root symbol and a different order of the variables).


, x k ) → r in P be a non-initial production such that pos A (r) = ∅.

for every other production ρ = → r in P and J ⊆ pos ρ ∅ = ρ for every production ρ , so every production besides the eliminated production ρ is preserved.


Evaluation We evaluated system output in two ways, i.e., automatically and in a user study.

we assume that if a tweet is retweeted it is relevant and is thus ranked higher over tweets that have not been reposted.


We propose a novel graphical model to simultaneously conduct NER and NEN on multiple tweets to address these challenges.

our model introduces a binary random variable for each pair of words with the same lemma across similar tweets, whose value indicates whether the two related words are mentions of the same entity.


With the fast growth of Web 2.0, a vast amount of user-generated content has accumulated on the social Web.

microblogging sites such as Twitter allow users to easily publish short instant posts about any topic to be shared with the general public.


However, there has been increasing interest in language grounding.

recent work has studied learning to act in an external environment by following textual instructions (Branavan et al., 2009(Branavan et al., , 2010(Branavan et al., , 2011Vogel and Jurafsky, 2010).


While we focus on the unsupervised setting, note that the model can also be used in a semi-supervised setting.

coarse (review-level) labels can be used to guide the induction of fine-grained latent structure (segment labels, alignments).


In a manually labeled random sample of recipe reviews, we find that refinement segments tend to be clustered together in certain reviews ("bursty"), rather than uniformly distributed across all reviews.

while we estimate that 23% of all segments are refinements, 42% of reviews do not contain any refinements.


The words that best discriminate the two discovered review classes are "savory ingredients" (chicken, pepper, meat, garlic, soup) and "baking/dessert ingredients" (chocolate, cake, pie, these, flour).

reviews naturally cluster by topics rather than whether they contain refinements.


In the sequence labeling task, feature functions across the sequence are often tied together.

feature functions at different locations of the sequence share the same parameter vector θ. Linear-chain CRF is a special case of the general CRFs.


Analysis of the latent user intent parameter Θ in Model IM showed that most people types had most of their probability mass assigned to the same three generic and common intents for people types: 'see pictures of', 'find biographical information about', and 'see video of'.

latent intents in Model IM are overexpressive and they do not help in differentiating people types.


Figure 1 illustrates the detailed process of generating words in the source language and target language respectively for the parallel corpus U , from the four mixture components in CLMM.

for each pair of parallel sentences u i ∈ U , we generate the words as follows.


Various textual and non-textual QA features are explored.

we explore four different types of contextual factors, namely, the information novelty and non-redundancy modeling for local and non-local sentence interactions under question segmentation.


However, when it comes to the more sophisticated multisentence questions, it would suffer from the problem of "incomplete answer".

such question often comprises several sub questions in specific contexts and the asker wishes to get elaborated answers for as many aspects of the question as possible.


Therefore we address the incomplete answer problem by developing a novel summarization technique taking different sub questions and contexts into consideration.

we want to learn a concise summary from a set of corresponding answers as supplement or replacement to the "best answer".


We utilize the probabilistic graphical model to solve the answer summarization task, Figure 1 gives some illustrations, in which the sites correspond to the sentences and the edges are utilized to model the interactions between sentences.

let x be the sentence sequence to all answers within a question thread, and y be the corresponding label sequence.


From this sub-set, we next select the questions with incomplete answers as defined in Section 2.1.

we select the questions where the average similarity between the best answer and all sub questions is less than 0.6 or when the star rating of the best answer is less than 4.


Section 4.3.1) reduce generation failure to 21% 6 and combining both corrections results in a failure rate of 13%.

error mining permits quickly identifying two issues which, once corrected, reduces generation failure by 20 points.


The key ideas are the modeling of word ngram probabilities with a maximum entropy model, and the use of word-class information in the definition of the features.

each word w is assigned deterministically to a class c, allowing the n-gram probabilities to be estimated as the product of class and word parts Both components are themselves maximum entropy n-gram models in which the probability of a word or class label l given history h is determined by The features f k (h, l) used are the presence of various patterns in the concatenation of hl, for example whether a particular suffix is present in hl.


We show the efficiency of these proposed algorithms for five NLP tagging tasks.

we show that iterative Viterbi A* decoding can be several times or orders of magnitude faster than the state-of-the-art algorithm for tagging tasks with a large number of labels.


Algorithm 2 shows the forward pass in which the node pruning is performed.

for any node, if the best score of sequence which passes such a node is less than the lower bound lb, such a node is removed from the lattice.


node n i represents the best sequence from the start to itself.


we first propose the A* and the iterative A* decoding algorithm for 1-best sequential decoding.


In such a case, A* is guaranteed to find the best sequence.

we start with the naive (but admissible) heuristic as follows the heuristic of node y t to the end is the sum of max edge scores between any two positions and max node scores per position.


We use the common features which are adopted in previous studies, for example (Sha and Periera, 2003).

we use the unigrams of the current and its neighboring words, word bigrams, prefixes and suffixes of the current word, capitalization, all-number, punctuation, and tag bigrams for POS, CoNLL2000 and CoNLL 2003 datasets.


We note that all decoding algorithms as listed in Section 3 and Section 4 are exact.


These parameters are non-negative and satisfy n θ size (n|h) = 1.

the size is drawn from a categorical distribution that is fully parameterized.


To incorporate the intuition that long distance dependencies are less likely, we impose a posterior constraint on dependency length.

we use the Posterior Regularization (PR) framework of Graça et al.


When performing unsupervised learning for the target language, we need to marginalize over possible derivations.

for the M step, we need probabilities of the form P (a modifies h|D l ; w t ).


Many of these words appeared as mention words for the Combined Cues corpus, indicating that prior intuitions about framing metalanguage were correct.

call (v), word(n), and term (n) were exceptionally frequent and effective at associating with mentioned language.


We argue that multilingual parallel data provides a valuable source of indirect supervision for induction of shallow semantic representations.

we consider unsupervised induction of semantic roles from sentences annotated with automatically-predicted syntactic dependency representations and use a stateof-the-art generative Bayesian non-parametric model.


Define functions ψ 1 , ψ 2 : Note that for all strings w 1 , w 2 ∈ Σ * , ψ(w 1 w 2 ) = ψ(w 1 )+ψ(w 2 ).

ψ is a homomorphism from the free monoid Σ * to Z × Z with addition as the monoid operation and (0, 0) as identity.


The second oracle ORACLE2 provides the performance of the projection model when gold-standard source entities are known, but the corresponding target entities still have to be determined by the projection model (gold-standard alignments are not known).

oRACLE2 is the projection model with all features, where in the test set we provide the gold standard English entities as input.


In this paper we overcome this issue by inducing clusters of pattern senses from feature representations of patterns.

we employ a topic model to partition entity pairs associated with patterns into sense clusters using local and global features.


It is difficult to discover a high-quality set of fine-grained entity types due to unknown criteria for developing such a set.

the optimal granularity of entity types depends on the particular pattern we consider.


Topic 5 shows the constitutional crisis in the states.

the anti-slavery state courts are prone to use the term "unconstitutional" much more often than the slave states.


Consequently, we propose a topic similarity model for hierarchical phrase-based translation (Chiang, 2007), where each synchronous rule is associated with a topic distribution.

• Given a document to be translated, we calculate the topic similarity between a rule and the document based on their topic distributions.


To achieve this goal, we use both source-side and target-side monolingual topic models, and learn the correspondence between the two topic models from word-aligned bilingual corpus.

we use two types of rule-topic distributions: one is source-side rule-topic distribution and the other is target-side rule-topic distribution.


Since our rerankers are simple binary classifiers of hypothesis pairs, they can encode complex dependencies thanks to kernel methods.

we used tree, sequence and linear kernels applied to structural and feature-vector representations describing hierarchical dependencies.


We achieve high reliability for most categories.

8 interesting is the fact that hearer-old entities (mediated/knowledge) can be identified reliably although all annotators had substantially different backgrounds.


The improvements using the additional local features over the original local classifiers are statistically significant in all cases.

the inclusion of semantic classes improves mediated/knowledge and mediated/func, and comparative anaphora are recognised highly reliably via a small set of comparative markers.


For property extraction this translates into the following observation: although we may need complex natural language processing for extracting properties from each and every item, simple patterns can extract most of the relevant properties from a subset of the items due to redundancy between sellers.

popular properties are used by many sellers and some of them write their descriptions in a manner that makes these properties easy to extract.


The confidence of rule {X → c} is denoted as θ(X − → c), and is the conditional probability of c given the termset X , that is, θ(X − → c) = σ(X ∪c) σ(X ) .

in this context, a classifier is denoted as R, and it is composed of a set of rules {X − → c} extracted from D. R is represented as a pool of entries with the form <key, properties>, where key={X , c} and properties={σ(X ), σ(X ∪ c), θ(X → c)}.


Entropy Minimization: Our method searches for a threshold α x min that provides the best entropy cut in the probability space induced by D x .


abilistic models; such models have recently been used to relax various assumptions of distant supervision Hoffmann et al., 2011).

they address the noisy assumption that, if two entities participate in a relation in a knowledge base, then all co-occurrences of these entities express this relation.


9 The output of this processing is a repository of structured objects (with POS tags, dependency parse, and entity types and mentions) for sentences from the training corpus.

for each pair of entity mentions (m 1 , m 2 ) in a sentence, we extract the following features F (m 1 , m 2 ): (1) the word sequence (including POS tags) between these mentions after normalizing entity mentions (e.g., replacing "John Nolen" with a place holder PER); if the sequence is longer than 6, we take the 3-word prefix and the 3-word suffix; (2) the dependency path between the mention pair.


In this work, we specifically examine feedback on the result of distant supervision.


We confirm hypothesis (1), but, surprisingly, are unable to confirm (2).

when using logistic regression to train relation extractors, increasing corpus size improves, consistently and significantly, the precision and recall produced by distant supervision, regardless of human feedback levels.


Since we are only interested in modeling dependencies between adjacent argument segments, we assign hard labels to each gap segment based on its contextual argument information.

the label of each gap segment is uniquely determined by its surrounding argument segments with a list representation.


Feature functions are factorized as products of two indicator functions: one defined on the input sequence (input features) and the other on the output labels (output features).

we could re-write For gap segments, we consider the following input feature templates: N-GRAM: Indicator function for n-gram appeared in the segment (n = 1, 2) ANCHOR: Indicator function for its relative position to the event anchor words (to the left, to the right, overlaps, contains) and the following output feature templates: 1STORDER: Indicator function for the combination of its immediate left argument and its immediate right argument.


We assume that there exists a projection from another decomposable function to κ.

we assume a collection of auxiliary functions, each of the form κ p : (x, y) → R, that scores a property p of the complete structure (x, y).


Note that the objective function in Equation 5, if written in the additive form, leads to a cost function reminiscent of the one used in constraint-driven learning algorithm (CoDL) (Chang et al., 2007) (and similarly, posterior regularization (Ganchev et al., 2010), which we will discuss later at Section 6).

in CoDL, the following cost function is involved in its EM-like inference procedure: where Y c defines the set of y's that all satisfy a certain constraint c, and d defines a distance function from y to that set.


To assess the importance of structured preference, we also perform experiments where structured preference information is incorporated at the inference time of the MaxEnt classifiers.

for each event, we first generate n-best lists for output structures.


when a structure contains a pattern, its score is incremented by 1.0.


We propose a set of features that model correspondence between the text spans assigned to each field and document structure.

these features model whether at least one of the spans mapped to each field appears in the email header; captures a full line in the document; is indent; appears within space lines; or in a tabular format.


The semantic features encoded therefore refer to date and time only.

these features indicate whether a unified set of tuples defines a value for all attributes; for example, in Figure 1,    day-of-week.


As shown in the table, removing the structural features hurt performance consistently across fields.

structure is informative for the title field, which is otherwise characterised with low content and contextual regularity.


Removal of the semantic features affected performance on the stime and etime fields, modeled by these features.

the optional etime field, which has fewer occurrences in the dataset, benefits from modeling semantics.


In contrast, the semantic features, which account for the semantic cohesiveness of the populated corp tuples, are shown to be necessary.

remov-ing them degrades the extraction of the abbreviated names; these features allow prediction of abbreviated names jointly with the full corporate names, which are more regular (e.g., include a distinctive suffix).


The coverage of 0.72 indicates that approximately 8 out of the 11 gold classes could be matched to a prevalent label.

8 clusters were labelled with a prevalent label corresponding to 8 distinct gold classes.


this clustering has many clusters with strong feature cohesion but a class structure that markedly differs from the gold.


When either normalisation or IDF weighting is left out, the cumulative micro-precision drops by up to 15 points (from 0.30 to 0.15 and 0.18) and the number of orphans increases from 67 up to 180.

clusters are less coherent in terms of features.


(the set of non-None available topics) expand via Word None non-terminals.

in the variant grammars topical words are generated with the following rule schema: In these variant grammars, the Word None nonterminal generates all the words of the language, so it defines a generic "background" distribution over all the words, rather than just the nontopical words.


In designing our study, we focus on a domain in which (i) there is rich use of language, some of which has achieved deep cultural penetration; (ii) there already exist a large number of external human judgments -perhaps implicit, but in a form we can extract; and (iii) we can control for the setting in which the text was used.

we use the complete scripts of roughly 1000 movies, representing diverse genres, eras, and levels of popularity, and consider which lines are the most "memorable".


We provide evidence that such cross-domain properties may hold, guided by one of our motivating applications in marketing.

we analyze a corpus of advertising slogans, and we show that these slogans have significantly greater likelihood at both the word level and the part-of-speech level with respect to a language model trained on memorable movie quotes, compared to a corresponding language model trained on non-memorable movie quotes.


We now discuss experiments that investigate the hypotheses discussed in §1.

we devise methods that can assess the distinctiveness and generality hypotheses and test whether there exists a notion of "memorable language" that operates across domains.


However, we can still use a language-modeling approach to assess whether the textual properties of the slogans are closer to the memorable movie quotes (as one would conjecture) or to the non-memorable movie quotes.

we train one language model on memorable quotes and another on non-memorable quotes   and compare how likely each slogan is to be produced according to these two models.


We also note that the higher likelihood of slogans under a "memorable language" model is not simply occurring for the trivial reason that this model predicts all other large bodies of text better.

the newswire section of the Brown corpus is predicted better at the lexical level by the language model trained on non-memorable quotes.


We also made some auxiliary observations that may be of interest.

we find differences in letter and sound distribution (e.g., memorable quotes -after curse-word removal -use significantly more "front sounds" (labials or front vowels such as represented by the letter i) and significantly fewer "back sounds" such as the one represented by u), 11 Feature set # feats Accuracy bag of words 962 59.67% distinctiveness 24 62.05% * generality 4 56.70% slogan sim.


f (e, C(v)) = 1, if e = ♣ and C(v).♥ = ♠ 0, else (3) where the symbol ♣ is a placeholder for a possible target translation (up to 4 words), the symbol ♥ indicates a contextual (lexical or semantic) element for the verbal predicate v, and the symbol ♠ represents the value of ♥.

lexical Features: The lexical element ♥ is extracted from the surrounding words of verbal predicate v. We use the preceding 3 words and the succeeding 3 words to define the lexical context for the verbal predicate Semantic Features: The semantic element ♥ is extracted from the surrounding arguments of verbal predicate v. we define a semantic window centered at the verbal predicate with 6 arguments are arguments on the left side of v while A 1 − A 3 are those on the right side.


Therefore we consider the reordering of an argument as the motion of the argument relative to its predicate.

we use the position of the predicate as the reference axis.


In this section, we elaborate how to integrate the two models into phrase-based SMT.

we integrate the models into a phrase-based system which uses bracketing transduction grammars (BTG) (Wu, 1997) for phrasal translation (Xiong et al., 2006).


We assume that each of the models is expert in some parts and so they do not necessarily agree on correct hypotheses.

product models (or LOPs) tend to have intersection-style effects while we are more interested in union-style effects.


All of these mixture operations were able to significantly improve over the concatenation baseline.

switching:Max could gain up to 2.2 BLEU points over the concatenation baseline and 0.39 BLEU points over the best performing baseline (i.e.


(2010) propose a similar method for machine translation that uses features to capture degrees of generality.

they include the output from an SVM classifier that uses the intersection between IN and OUT as positive examples.


(2010), a generalization of consensus or minimum Bayes risk decoding where the search space consists of those of multiple systems, in that model combination uses forest of derivations of all component models to do the combination.

it requires all component models to fully decode each sentence, compute n-gram expectations from each component model and calculate posterior probabilities over translation derivations.


From the results, we can see that both the loose and tight decoders outperform the baseline decoders and the improvement is significant using the sign-test of Collins et al.

(2005)  the loose model has a better performance while the tight model has a faster speed.


For non-terminal productions, we back off from r to P and its parent P , and then to just P .

we back off from a rule-annotated grammar p(C d 1 |P, P , r ) to a parent-annotated grammar (Johnson, 1998)  In order to generalize to unseen rule yields C d 1 , we further back off from the basic PCFG probability In other words, we back off from a raw PCFG to where λ = 0.9 is an interpolation constant.


Cherry and Quirk (2008) report an accuracy of 71.9% on a similar experiment with German a source language, though the translation system and training data were different so the numbers are not comparable.

their translations had a lower BLEU score, making their task easier.


First, we assume that for all multilingual text, every text portion is written in one of the given languages; there is no input text of an unknown language without learning data.


Since this value is additive and gives the total code length of X for language Y , we adopt this value in our approach.

considering the additive characteristic of the description length formulated previously as formula (1), we denote the minimized description length for a given text X simply as DP (X), which can be decomposed recursively as follows 6 : the computation of DP (X) is decomposed into obtaining the addition of two terms by searching through t ∈ {0, .


Multilingual texts were needed only to test the performance of the proposed method.

we trained the model only through monolingual data, as mentioned above.


In recent years there has been a growing interest in crowdsourcing methodologies to be used in experimental research for NLP tasks.

evaluation of systems and theories about persuasion is difficult to accommodate within existing frameworks.


In recent years there has been a growing interest in finding new cheap and fast methodologies to be used in experimental research, for, but not limited to, NLP tasks.

approaches to NLP that rely on the use of web tools -for crowdsourcing long and tedious tasks -have emerged.


This methodology allows us to crowdsource experiments with thousands of subjects for a few euros in a few hours, by tweaking and using existing commercial tools for advertising on the web.

we make reference to the AdWords Campaign Experiment (ACE) tool provided within the Google AdWords suite.


To test the extent to which AdWords can be exploited, we focused on how to evaluate lexical variations of a message.

we were interested in gaining insights about a system for affective variations of existing commentaries on medieval frescoes for a mobile museum guide that attracts the attention of visitors towards specific paintings (Guerini et al., 2008;Guerini et al., 2011b).


We ran two pilot experiments to test how affective variations of existing texts alter their persuasive im-pact.

we were interested in gaining initial insights about an intelligent system for affective variations of existing commentaries on medieval frescoes.


We say that a word has a (mostly) positive (negative) polarity if the majority sense of the word is positive (negative).

a word has a mostly positive polarity if P + > P − + P 0 and it has a mostly negative polarity if P − > P + + P 0 .


The question to be answered is: Given an assignment of polarities to the words, does there exist an assignment of polarities to the synsets that agrees with that of the words?

given the polarities of a subset of words (e.g., that given by one of the three SWDs) the problem of finding the polarities of the synsets that agree with this assignment is a "hard" problem.


Hence, where M DS(w) is the set of all MDSs of w. For each MDS T of w, the clause C(w, p, T ) is the AND of the variables corresponding to polarity p of the synsets in T .

the formula Φ is not in CNF after this construction and it needs to be converted.


In addition, the intention of SAT solver designers is to use MUCs in a interactive manner.

the errors pointed out by a MUC are corrected and then the new improved formula is re-evaluated by the SAT solver.


Sentence simplification can be defined as the process of producing a simplified version of a sentence by changing some of the lexical material and grammatical structure of that sentence, while still preserving the semantic content of the original sentence, in order to ease its understanding.

language learners (Siddharthan, 2002), people with reading disabilities (Inui et al., 2003) such as aphasia (Carroll et al., 1999), and low-literacy readers (Watanabe et al., 2009) can benefit from this application.


This definition follows the fact that POS tags in the same category form similar syntax structures in a sentence (Zhao and Marcus, 2009).

inter-category errors are treated as serious errors, while intra-category errors are treated as minor errors.


(2011), we build our joint model to solve word segmentation, POS tagging, and dependency parsing within a single framework.

we change the role of the shift action and additionally use the append action, inspired by the character-based actions used in the joint segmentation and POS tagging model by Zhang and Clark (2010).


Our models with dictionaries (those marked with '(d)') have competitive accuracies to other state-ofthe-art systems, and SegTagDep(d) achieved the best reported segmentation and POS tagging accuracies, using no additional corpora other than the dictionaries.

the POS tagging accuracy is more than 0.4% higher than the previous best system thanks to the contribution of syntactic dependencies.


The model demonstrated substantial improvements on the three tasks over the pipeline combination of the state-of-the-art joint segmentation and POS tagging model, and dependency parser.

results showed that the  Table 6: Final results on CTB-6 and CTB-7 accuracies of POS tagging and dependency parsing were remarkably improved by 0.6% and 2.4%, respectively corresponding to 8.3% and 10.2% error reduction.


In morphologically rich languages (MRLs) substantial information about the grammatical relations between entities is expressed at word level using inflectional affixes.

in MRLs such as Hebrew, Arabic, Turkish or Maltese, elements such as determiners, definite articles and conjunction markers appear as affixes that are appended to an openclass word.


Instead of collapsing all non-terminals in the source language into a single symbol X as in Chiang (2007), given a word sequence f i j from position i to position j, we first find heads and then concatenate the POS tags of these heads as f i j 's non-terminal symbol.

we adopt unlabeled dependency structure to derive heads, which are defined as: is regarded as a head if it is dominated by a word outside of this sequence.


This section describes a fully unsupervised solution to this problem, computing the probability that a pattern supports a given relation, which will allow us to determine the most likely relation expressed in any sentence.

if a sentence contains two entities, e i and e j , connected through a pattern w, our model computes the probability that the pattern is expressing any relation -P (r|w)for any relation r defined in the knowledge base.


We demonstrate the need to rethink resources, features and methods of learning temporal relations between events in different domains with the help of experiments in learning temporal relations in clinical text.

we observe that we get better results in learning to rank chains of medical events to derive temporal relations (and their inverses) than learning a classifier for the same task.


During our first experiment, we also attempted to capture more nuanced learning curves than the linear case.

we anticipated that learning over time should follow an S-shaped curve.


The motivation of this paper is to investigate the impact of these non-isomorphic dependency structures to be used for SMT.

we would like to provide a comparative evaluation of these dependencies in a string-to-dependency decoder (Shen et al., 2008).


Our conversion preserves more sentences under round-trip conversion (51.1% vs. 39.6%) and is more robust.

unlike past methods, ours does not require ad-hoc rules over non-local features, and so can be easily integrated into a parser.


To test this distinction we look for correlation between conversion quality and parse difficulty on another metric.

figure 3 (right) shows CCG labeled dependency performance for the C&C parser vs. CCGbank conversion PARSE-VAL scores.


The languages of the parallel corpus are considered as mutual semantic tags: As the meaning of a sentence stays constant during translation, we are able to resolve ambiguities which exist in only one of the langauges by only accepting those interpretations which are licensed by the other language.

we select one language as the target language, translate the other language's semantics for every parse into the target language and thus align maximally similar semantic representations.


's findings that D U and D T serve better than frequency as predictors of word fate, for the set of anglicisms (Table 3), all correlations were both negative and stronger, and initial frequency log 10 f t (not dissemination) is the best predictor, especially as the time spans increase in length.

while most words' frequency change cannot generally be predicted from earlier frequency, we find that, for anglicisms, a high frequency is more likely to lead to a decline, and vice versa.


At last, we compare wmfvec with a mature WSD system based on sense similarities, (3) (Sinha and Mihalcea, 2007) [jcn+elesk], where they evaluate six sense similarities, select the best of them and combine them into one system.

in their implementation they use jcn for noun-noun and verbverb pairs, and elesk for other pairs.


Finally, the patterns observed in many different query pairs are kept.

we rely on the frequency of a pattern to filter noisy patterns.


In this paper, we propose to support participants of online debates with a framework combining Textual Entailment (TE) (Dagan et al., 2009) and abstract argumentation theory (Dung, 1995).

tE is adopted to extract the abstract arguments from natural language debates and to provide the relations among these arguments; argumentation theory is then used to compute the set of accepted arguments among those obtained from the tE module, i.e., the arguments shared by the majority of the participants without being attacked by other accepted arguments.


In comparison to (Talukdar et al., 2012), our ILP encoding is time-scale invariant.

for the same data, if the granularity of T is changed from months to seconds, for example, the size of the ILP is not affected.


In this work, we provide a comparison of techniques for selecting snippets associated with the query, as well as an evaluation of methods for aligning the matching words in the query to those in the selected snippets.

for each query 1 with a corresponding set of "relevant snippets," we first apply the baseline tagger to the query and all the snippets.


They report report significant improvements when incorporating features from the snippets.

they exploit capitalization and query terms matching URL components; both of which we have used in this work.


This paper presents the problem within Hittite and Ancient Near Eastern studies of fragmented and damaged cuneiform texts, and proposes to use well-known text classification metrics, in combination with some facts about the structure of Hittite-language cuneiform texts, to help classify a number of fragments of clay cuneiform-script tablets into more complete texts.

i propose using Sumerian and Akkadian ideogrammatic signs within Hittite texts to improve the performance of Naive Bayes and Maximum Entropy classifiers.


The classification algorithm we use is inspired by previous work on datadriven word sense disambiguation.

we use a system that integrates both local and topical features.


However, to the best of our knowledge, DADT obtained the best accuracy for a fully-supervised method that uses only unigram features.

kourtis and Stamatatos (2011), who obtained the highest accuracy (65.8%), assumed that all the test texts are given to the classifier at the same time, and used this additional information with a semi-supervised method; while kern et al.


IMAM exploits a multi-way-clustering-based classification scheme to simultaneously cluster documents, words and links into their respective clusters.

the word and link clusterings can automatically associate the correlated features from different domains.


While straightforward, inference isn't cheap.

for interactive settings (Hu et al., 2011), efficient inference would improve perceived latency.


The source side src(r i ) and the target side tgt(r i ) of each phrase translation rule r i in d* form a translation span pair {src(r i )<=>tgt(r i )} of (c,e).

the TSA of (c,e) is a set of translation span pairs generated from phrase translation rules used by the best derivation d*.


Until now, a natural way is to use TSAs to directly improve word alignment quality by deleting some spurious links and adding some new confident links, which in turn improves rule quality and translation quality.

if a desirable translation rule was blocked due to some spurious links, we will output this translation rule.


However, this method may not result in the optimal reduction, since each redundancy scores are estimated based on the assumption of the existence of all the other entries.

there are dependency relationships among entries.


The first reason is that Japanese is a typical headfinal language.

a syntactic head word comes after nonhead (dependent) words.


Our basic conclusion, then, is that the parses that help translation (according to Bleu) are, on average, worse parses.

forest-to-string translation hurts parsing.


If we optimize our system to maximize labeled-bracket F1 instead, we find that our translation system selects parses that score higher than the baseline parser's.

forest-tostring translation can help parsing.


Table 3c shows that this system still outperforms the original parser.

part of the gain is not attributable to translation, but additional source-side context and data that the translation model happens to capture.


In this paper, we show that an unsupervised segmenter can in fact rival or surpass supervised MSA segmenters on MSA itself, while at the same time providing superior performance on dialect.

we compare the state-of-the-art morphological analyzer of Lee et al.


What is more surprising is that the Lee segmenter compares favorably with the supervised MSA segmenters on MSA itself.

the Lee segmenter with MM yields higher BLEU scores than does MADA, a leading supervised segmenter, while preserving almost the same performance as GS on dialect.


We extract hidden topic category information to model topic signature.

we exploit the Latent Dirichlet Allocation (LDA) method (Blei et al., 2003), which is a widely used topic modeling technique, to decompose the topic-word matrix TW into hidden topic categories: TW = TH * HW , where TH is a topic-hidden matrix, HW is hiddenword matrix, and h is the manually-chosen parameter to determine the size of hidden topic categories.


The positive diffusion records are generated based on the post-response behavior.

if a person x posts a message containing one of the selected topic t, and later there is a person y responding to this message, we consider a diffusion of t has occurred from x to y (i.e., (x, y, t) is a positive instance).


Studies on spelling error generation mechanisms are found in earlier work such as Cooper (1983).

grudin (1983) offers a detailed study of the errors generated in the transcription typing scenario, where the subjects are asked to transcribe a text without correcting the errors they make.


In Substitution errors for all data we analyzed, substituting for the character that appeared before, or are to appear in the word was common ( Figure  9).

in en keystroke and ja keystroke, look-ahead errors are much more common than nonlook-ahead errors.


10 Since explicit character links between each I and O will only be established at match or capture group boundaries, any text from the left-hand side of a rule that should appear in O must be explicitly linked through a capture group reference (rather than merely written out in the righthand side of the rule).

rule (1) above should be preferred to the following variant (which would result in character start and end offsets of 0 and 5 for both output tokens): !won't will n't (2) During rule application, we keep track of character start and end positions as offsets between a string before and after each rule application (i.e.


The major difference is that our hypergraph is not a phrasal lattice because each phrase pair is associated with a dependency structure on the target side.

our second pass is to find the Viterbi derivation with addition features rather than parsing the phrasal lattice.


Firstly, all of them determine whether those matched segments should be adopted or not at sentence level.

they are either all adopted or all abandoned regardless of their individual quality.


Indeed, it is widely recognized that -with proper initialization -fertility based models outperform sequence based ones.

sequence based models can simply ignore a part of the sentence to be conditioned on, while fertility based models explicitly factor in a probability of words in this sentence to have no aligned words (or any other number of aligned words, called the fertility).


We posit that this perspective is too limiting, especially with respect to linguistic data, where each individual's idiolect and linguistic background can give rise to many different -and yet equally valid -truths.

in highly subjective annotation tasks, the differences between annotators cannot be captured by simple models such as scaling all instances of a certain annotator by a factor.


GP regression also permits an analytic formulation of the marginal likelihood, p(y|X) = f p(y|X, f )p(f ), which can be used for model training (X are the training inputs).

we can derive the gradient of the (log) marginal likelihood with respect to the model hyperparameters (i.e., a, σ n , σ s etc.)


Marginal distribution constraints attempt to match the expected frequency of an n-gram with its observed frequency.

if we use the model to randomly generate a very large corpus, the n-grams should occur with the same relative frequency in both the generated and original (training) corpus.


The number of added arcs is no more than found in the original model.

we are summing over all possible histories of length n in equation 8, and the steady state probability calculation outlined in the previous section includes the probability mass for histories h ∈ G. The probability mass of states not in G ends up being allocated to the state representing their longest suffix that is explicitly in G. the state that would be active when these histories are encountered.


These two terms refer to the two quantified terms introduced by the plural chunk 1p: 1c (for collection) represents the set (or in better words collection) of entities, defined by the plural, and 1d (for distribution) refers to the implicit universal, introduced by the plural.

for a plural chunk ip, id represents the universally quantified entity over the collection ic.


In computing AR, a sentence counts as correct iff all the outscoping relations have been recovered correctly -in other words, iff R = 100%, regardless of the value of P. AR may be more practically meaningful, because if in the correct scoping of the sentence there is no outscoping between two elements, inserting one does not affect the interpretation of the sentence.

precision is less important for QSD in practice.


Each preterminal consists of a type and a value; neither which are lexically informed.

the word week and preterminal (Week, Duration) are not tied in any way.


there are two modes of expressing dates which resist intersection: a day-of-month-based mode and a week-based mode.


Each rule of the parse derivation was assigned a score according to a log-linear factor.

each rule R = ( with features over the rule and derivation so far φ(R), subject to parameters θ, is given a probability: Naïvely, this parsing algorithm gives us a complexity of O(n 3 k 2 ), where n is the length of the sentence, and k is the size of the beam.


Bracketing Features A feature is defined over every nonterminal combination, consisting of the pair of children being combined in that rule.

let us consider a rule Unlike syntactic parsing, child types of a parse tree uniquely define the parent type of the rule; this is a direct consequence of our combination rules being functions with domains defined in terms of the temporal types, and therefore necessarily projecting their inputs into a single output type.


To enforce agreement of a particular property, we require a mention to have the same property value as its antecedent.

for mentions i and j, if a i = j, we want to ensure that p i and p j agree.


Commonly-used coreference metrics such as MUC (Vilain et al., 1995) and B 3 (Bagga and Baldwin, 1998) do not have this property, so we instead make use of a parameterized loss function that does and fit the parameters to give good performance.

we take where c 1 , c 2 , and c 3 are real-valued weights, K 1 denotes the event that a i is falsely anaphoric when it should be non-anaphoric, K 2 denotes the event that a i is falsely non-anaphoric when it should be anaphoric, and K 3 denotes the event that a i is correctly determined to be anaphoric but .


Therefore, we use our BA-SIC model to prune antecedent choices for each a i in order to reduce the size of the factor graph that we must instantiate.

we prune links between pairs of mentions that are of mention distance more than 100, as well as values for a i that fall below a particular odds ratio threshold with respect to the best setting of that a i in the BASIC model; that is, those for which is below a cutoff γ.


7 We even do this for the BASIC model, since we found that performance of the pruned and retrained model was generally higher.

8 the distribution used is a Dirichlet with α = 3.5 for the true underlying cluster and α = 1 for other values, chosen so that 25% of samples from the distribution did not have the correct mode.


Using our annotations, we can extend CTBstyle syntax trees (Figure 1(a)) into characterlevel trees (Figure 1(b)).

we mark the original nodes that represent POS tags in CTBstyle trees with "-t", and insert our word structures as unary subnodes of the "-t" nodes.


The feature templates in bold are novel, are designed to encode head character information.

the indict function denotes whether a word is in a tag dictionary, which is collected by extracting all multi-character subwords that occur more than five times in the training corpus.


This figure can be useful for high-level applications that make use of character-level trees by 4 http://sourceforge.net/projects/zpar/, version 0.5. our parser, as it reflects the capability of our parser in analyzing word structures.

the performance of parsing OOV word structure is an important metric of our parser.


We can evaluate any term t in T ∆ (X k ) in A, to obtain a k-ary operation t A over the domain.


We take the approach in Jiang et al., 2012), which uses a point estimate of topics Φ from training data and makes prediction based on them.

we use the MAP estimateΦ to replace the probability distribution p(Φ).


3 can be addressed with dual decomposition, a class of optimization techniques that tackle the dual of combinatorial problems in a modular, extensible, and parallelizable manner (Komodakis et al., 2007;Rush et al., 2010).

we employ alternating directions dual decomposition (AD 3 ; Martins et al., 2011a for solving a linear relaxation of Eq.


We performed error analysis on our development set by randomly choosing 100 word errors from the WER alignment and manually annotating them with relevant features.

for each word error we recorded whether or not the error contained punctuation (either in the predicted word or the gold word), whether the text in the corresponding portion of the original image was italicized, and whether the corresponding portion of the image exhibited over-inking, missing ink, or significant ink blotches.


In the navigation task, action arguments frequently contain redundant, rarely used information.

the interleaving verification steps frequently include many details that are never actually mentioned in the NL instructions.


A major weakness of many existing scoring engines such as the Intelligent Essay Assessor TM (Landauer et al., 2003) is that they adopt a holistic scoring scheme, which summarizes the quality of an essay with a single score and thus provides very limited feedback to the writer.

it is not clear which dimension of an essay (e.g., style, coherence, relevance) a score should be attributed to.


These results are consistent with our intuition that each of the enumerated error classes has a negative impact on thesis clarity score.

each has a demonstrable negative impact, costing essays an average of more than 0.59 points when it occurs.


While we employ seven types of features (see Sections 4.2 and 4.3), only the word n-gram features are subject to feature selection.

2 we employ the top n i n-gram features as selected according to information gain computed over the training data (see Yang and Pedersen (1997) for details).


Since they are mainly structural elements, they are more heavily affected by differences in the syntax and grammar of Italian and LIS (and, presumably, in those of any spoken language and the "corresponding" SL).

as we will see later, some connectives are translated with a sign, some connectives are dropped, whereas others affect the positioning of other signs, or just their syntactic proximity.


One can even have a movement verb where the starting and ending positions of the gesture are positioned independently to indicate the source and destination of the movement.

these morphological variations can perform a similar function to gender and number agreement in Italian backreferences, but they can also assume roles that in Italian would be performed by prepositions, which are connectives.


The use of adjacency is standard in DMV and enables us to have different P dmv stop for situations when no child was generated so far (adj = 1).

p dmv stop (c h , dir , adj = 1, c f ) decides whether the word c h has any children in the direction dir at all, whereas p dmv stop (h, dir , adj = 0, c f ) decides whether another child will be generated next to the already generated one.


To this end, our model jointly parses the two corpora according to the corresponding annotations, enabling transfer via parameter sharing.

we augment each target tree node with hidden variables that capture the connection to the coarse annotations.


In particular, we augment each target tree node with hidden variables that capture the connection to the coarse annotations.

each node in the target tree has two labels: an entry which is specific to the target formalism, and a latent label containing a value from the Penn Treebank tagset, such as NP (see Figure 2).


All of these corpora were derived via conversion of Penn Treebank to the target formalisms.

our CCG and HPSG datasets were converted from the Penn Treebank based on hand-  Figure 4: Model performance with 500 target formalism trees and different numbers of CFG trees, evaluated using labeled/unlabeled dependency F-score and unlabeled PARSEVAL.


We noticed, however, that the resulting values are consistent across different formalisms.

we set the l 2 -norm weight to λ = 1.0, the supertagger threshold to β = 0.01, and the PCFG pruning threshold to α = 0.002.


To analyze the effectiveness of annotation transfer, we fix the number of annotated trees in the target formalism and vary the amount of coarse annotations available to the algorithm during training.

we use 500 sentences with formalism-specific annotations, and vary the number of CFG trees from zero to 15,000.


Second, the OSTAG form of adjunction is consistent with the TIG form.

we can extend OSTAG by allowing on-spine adjunction of left-or right-auxiliary trees in keeping with the TIG constraints without increasing generative capacity.


In contrast, we need only select an appropriate regularization strength λ.

when r(w) = λ w 1 , the closedform solution to 14is is the clipping function that in this case sets a weight to 0 when it falls below the threshold η t−1 λ.


f 4 is linked to e 5 and e 6 , only f 4 − e 5 is kept.

we use this strategy to guarantee that every source word appears only once in the source decoding sequence.


The perplexity of the test corpus for reordering model comparison is much lower than those NIST corpora for translation experiments.

there exists mismatch of the data for reordering model training and actual MT data.


To train the model, we use Gibbs sampling, a Markov Chain Monte Carlo (MCMC) technique for posterior inference.

we seek to infer the latent sequence of translation decisions given a corpus of sentence pairs.


Then, for a given target class and labeled data set, we utilize the statistics to improve a classifier.

we introduce a method that extends Multinomial Naive Bayes (MNB) to leverage marginal probability statistics P (w) of each word w, computed over the unlabeled data.